Evaluation of irradiance metrics with respect to predicting sapling growth
Bibliographic record
Abstract
Accurate and efficient measurement of photosynthetically active radiation (PAR) is critical in ecological studies. We evaluated 25 metrics of PAR with respect to predicting growth of sugar maple ( Acer saccharum Marsh.) and white ash ( Fraxinus americana L.) saplings from understory to large gap conditions. PAR metrics were derived from gallium arsenide photodiodes, hemispherical canopy photographs (film and digital), and a LI-COR LAI-2000 plant canopy analyzer. In general, percent canopy openness, estimated with film photographs or LAI-2000, best predicted growth. Mean daily photosynthetic photon flux density (PPFD) from photodiodes ranked intermediate; direct beam radiation (from digital and film photographs) was among the poorest growth predictors. Metrics that integrate direct radiation may be relatively poor predictors of growth, because sunflecks were above PPFD levels at which photosynthesis saturates but fully contributed to the calculation of mean daily PPFD. Mean daily PPFD based on truncated sunflecks (to PPFD levels at which photosynthesis saturates) improved predictions of white ash radial growth. Film canopy photographs and the LAI-2000 had relatively low measurement error (indicated by repeatability). High contrast in film photographs, compared to digital, reduced ambiguity in manual thresholding. From a plant-centered perspective, percent canopy openness measured with either the LAI-2000 or film hemispherical photographs provided the best growth predictions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".